The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Abstract
To determine key factors that affect a user's behavior with search results, we conducted a controlled eye-tracking study of users completing search tasks using both desktop and mobile devices. We focus our investigation on users' behavior from their query to the first action they take with the search engine results page (SERP): either a click on a search result or a reformulation of their query. We found that a user deciding to reformulate a query rather than click on a result is best understood as being caused by the user's examination pattern not including a relevant search result. If a user sees a relevant result, they are very likely to click it. Of note, users do not look at all search results and their examination may be influenced by other factors. The key factors we found to explain a user's examination pattern are: the rank of search results, the user type, and the query quality. While existing research has identified rank and user types as important factors affecting examination patterns, to our knowledge, query quality is a new discovery. We found that user queries can be understood as either of weak or strong quality. Weak queries are those that the user may believe are more likely to fail compared to a strong query, and as a result, we find that users modify their examination patterns to view fewer documents when they issue a weak query, i.e. they give up sooner.
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Cites
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Accurately interpreting clickthrough data as implicit feedback
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005) · Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1076034.1076063 · Cited by 1,408
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Large Language Models for Information Retrieval: A Survey
Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., & Wen, J.-R. (2026) · ACM Transactions on Information Systems · doi:10.1145/3748304 · Cited by 225
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Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
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Metadata
- Publisher
- ACM
- Published
- 2019-11-03
- OpenAlex
- W1602136775
- Cited by
- 1,287
- References
- 2
- Type
- paratext
- Language
- en
- Open access
- closed
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